995 resultados para Gaussian Fields


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In this work, two families of asymptotic near-tip stress fields are constructed in an elastic-ideally plastic FCC single crystal under mode I plane strain conditions. A crack is taken to lie on the (010) plane and its front is aligned along the [(1) over bar 01] direction. Finite element analysis is first used to systematically examine the stress distributions corresponding to different constraint levels. The general framework developed by Rice (Mech Mater 6:317-335, 1987) and Drugan (J Mech Phys Solids 49:2155-2176, 2001) is then adopted to generate low triaxiality solutions by introducing an elastic sector near the crack tip. The two families of stress fields are parameterized by the normalized opening stress (tau(A)(22)/tau(o)) prevailing in the plastic sector in front of the tip and by the coordinates of a point where elastic unloading commences in stress space. It is found that the angular stress variations obtained from the analytical solutions show good agreement with finite element analysis.

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The system equations of a collisionless, unmagnetized plasma, contained in a box where a high frequency (HF) electric field is incident, are solved in the electrostatic approximation. The surface modes of the plasma in the semi-infinite and box geometry are investigated. In thi high frequency limit, the mode frequencies are not significantly changed by the HF field but their group velocities can be quite different. Two long wavelength low frequency modes, which are not excited in the absence of HF field, are found. These modes are true surface modes (decaying on one wavelength from the surface) unlike the only low frequency ion acoustic mode in the zero field case. In the short wavelength limit the low frequency mode occurs at omega i/ square root 2, omega i being the ion plasma frequency, as a result similar to the case of no HF field.

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The stability characteristics of a Helmholtz velocity profile in a stratified Boussinesq fluid in the presence of a rigid boundary is studied, A jump in the magnetic field is introduced at a level different from the velocity discontinuity. New unstable modes in addition to the Kelvin-Helmhottz mode are found. The wavelengths of these unstable modes are close to the wavelengths of internal Alfv6n gravity waves in the atmospher.

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Formative time lags in nitrogen, oxygen, and dry air are measured with and without a magnetic field over a range of gas pressures (0.05 ' p ' 20.2 torr 5 kPa to 2 MPa, electric field strengths (1.8xO14 EEs 60xlO V m l) and magnetic field strengths (85xl0-4 < B ' 16x10-2 Tesla). For experiments below the Paschen minimum, the electrodes are designed to ensure that breakdown occurs over longer gaps and for experiments above the Paschen minimum, a coaxial cylindrical system is employed. The experimental technique consists of applying pulse voltages to the gap at various constant values of E/p and B/p and measuring the time lags from which the formative time lags are separated. In the gases studed, formative time lags decrease on application of a magnetic field at a given pressure for conditions below the Paschen minimum. The voltages at which the formative time lags remain the same without and with magnetic fields are determined, and electron molecule collision frequencies (v/p) are determined using the Effective Reduced Electric Field [EREF] concept. With increasing ratio of E/p in crossed fields, v/p decreases in all the three gases. Measurements above the Paschen minimum yield formative time lags which increase on application of a magnetic field. Formative time lags in nitrogen in ExB fields are calculated assuming an average collision frequency of 8.5x109 sec-1 torr 1. It is concluded that the EREF concept can be applied to explain formative time lags in ExB fields.

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Anisotropic gaussian beams are obtained as exact solutions to the parabolic wave equation. These beams have a quadratic phase front whose principal radii of curvature are non-degenerate everywhere. It is shown that, for the lowest order beams, there exists a plane normal to the beam axis where the intensity distribution is rotationally symmetric about the beam axis. A possible application of these beams as normal modes of laser cavities with astigmatic mirrors is noted.

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An exact expression for the calculation of gaussian path integrals involving non-local potentials is given. Its utility is demonstrated by using it to evaluate a path integral arising in the study of an electron gas in a random potential.

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Pseudo-marginal methods such as the grouped independence Metropolis-Hastings (GIMH) and Markov chain within Metropolis (MCWM) algorithms have been introduced in the literature as an approach to perform Bayesian inference in latent variable models. These methods replace intractable likelihood calculations with unbiased estimates within Markov chain Monte Carlo algorithms. The GIMH method has the posterior of interest as its limiting distribution, but suffers from poor mixing if it is too computationally intensive to obtain high-precision likelihood estimates. The MCWM algorithm has better mixing properties, but less theoretical support. In this paper we propose to use Gaussian processes (GP) to accelerate the GIMH method, whilst using a short pilot run of MCWM to train the GP. Our new method, GP-GIMH, is illustrated on simulated data from a stochastic volatility and a gene network model.

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This paper considers the applicability of the least mean fourth (LM F) power gradient adaptation criteria with 'advantage' for signals associated with gaussian noise, the associated noise power estimate not being known. The proposed method, as an adaptive spectral estimator, is found to provide superior performance than the least mean square (LMS) adaptation for the same (or even lower) speed of convergence for signals having sufficiently high signal-to-gaussian noise ratio. The results include comparison of the performance of the LMS-tapped delay line, LMF-tapped delay line, LMS-lattice and LMF-lattice algorithms, with the Burg's block data method as reference. The signals, like sinusoids with noise and stochastic signals like EEG, are considered in this study.

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Gaussian processes (GPs) are promising Bayesian methods for classification and regression problems. Design of a GP classifier and making predictions using it is, however, computationally demanding, especially when the training set size is large. Sparse GP classifiers are known to overcome this limitation. In this letter, we propose and study a validation-based method for sparse GP classifier design. The proposed method uses a negative log predictive (NLP) loss measure, which is easy to compute for GP models. We use this measure for both basis vector selection and hyperparameter adaptation. The experimental results on several real-world benchmark data sets show better orcomparable generalization performance over existing methods.

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This study addresses four issues concerning technological product innovations. First, the nature of the very early phases or "embryonic stages" of technological innovation is addressed. Second, this study analyzes why and by what means people initiate innovation processes outside the technological community and the field of expertise of the established industry. In other words, this study addresses the initiation of innovation that occurs without the expertise of established organizations, such as technology firms, professional societies and research institutes operating in the technological field under consideration. Third, the significance of interorganizational learning processes for technological innovation is dealt with. Fourth, this consideration is supplemented by considering how network collaboration and learning change when formalized product development work and the commercialization of innovation advance. These issues are addressed through the empirical analysis of the following three product innovations: Benecol margarine, the Nordic Mobile Telephone system (NMT) and the ProWellness Diabetes Management System (PDMS). This study utilizes the theoretical insights of cultural-historical activity theory on the development of human activities and learning. Activity-theoretical conceptualizations are used in the critical assessment and advancement of the concept of networks of learning. This concept was originally proposed by the research group of organizational scientist Walter Powell. A network of learning refers to the interorganizational collaboration that pools resources, ideas and know-how without market-based or hierarchical relations. The concept of an activity system is used in defining the nodes of the networks of learning. Network collaboration and learning are analyzed with regard to the shared object of development work. According to this study, enduring dilemmas and tensions in activity explain the participants' motives for carrying out actions that lead to novel product concepts in the early phases of technological innovation. These actions comprise the initiation of development work outside the relevant fields of expertise and collaboration and learning across fields of expertise in the absence of market-based or hierarchical relations. These networks of learning are fragile and impermanent. This study suggests that the significance of networks of learning across fields of expertise becomes more and more crucial for innovation activities.

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The Gaussian probability closure technique is applied to study the random response of multidegree of freedom stochastically time varying systems under non-Gaussian excitations. Under the assumption that the response, the coefficient and the excitation processes are jointly Gaussian, deterministic equations are derived for the first two response moments. It is further shown that this technique leads to the best Gaussian estimate in a minimum mean square error sense. An example problem is solved which demonstrates the capability of this technique for handling non-linearity, stochastic system parameters and amplitude limited responses in a unified manner. Numerical results obtained through the Gaussian closure technique compare well with the exact solutions.

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It is shown that the conclusions arrived at regarding the instability of an incompressible fluid cylinder in the presence of the magnetic field and the streaming velocity in a recent communication easily follow from the study of propagation characteristics of Alfvén surface waves along cylindrical plasma columns made earlier.

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Elasmobranchs are under increasing pressure from targeted fisheries worldwide, but unregulated bycatch is perhaps their greatest threat. This study tested five elasmobranch bycatch species (Sphyrna lewini, Carcharhinus tilstoni, Carcharhinus amblyrhynchos, Rhizoprionodon acutus, Glyphis glyphis) and one targeted teleost species (Lates calcarifer) to determine whether magnetic fields caused a reaction response and/or change in spatial use of an experimental arena. All elasmobranch species reacted to magnets at distances between 0.26 and 0.58 m at magnetic strengths between 25 and 234 gauss and avoided the area around the magnets. Contrastingly, the teleosts showed no reaction response and congregated around the magnets. The different reactions of the teleosts and elasmobranchs are presumably driven by the presence of ampullae of Lorenzini in the elasmobranchs; different reaction distances between elasmobranch species appeared to correlate with their feeding ecology. Elasmobranchs with a higher reliance on the electroreceptive sense to locate prey reacted to the magnets at the greatest distance, except G. glyphis. Notably, this is the only elasmobranch species tested with a fresh- and saltwater phase in their ecology, which may account for the decreased magnetic sensitivity. The application of magnets worldwide to mitigate the bycatch of elasmobranchs appears promising based on these results.